Kevin Antonio Steiner

EPFLETUEDOCEDRS

Kevin is a PhD student at École Polytechnique Fédérale de Lausanne (EPFL). He joined the Intelligent Maintenance and Operations Systems (IMOS) Lab under the supervision of Prof. Olga Fink in February 2023. Before joining IMOS, he obtained his master's degree in physics from the Karlsruhe Institute of Technology, where he conducted research in machine learning methods for accelerating chemical reaction exploration.

During the PhD he will focus on physics-informed machine learning methods and its applications to composite structures. Focusing on multiple aspects of composite damage, from detection to design optimization his goal is to improve the reliability of composite materials through improved monitoring and predictive models.

Education

Master of Science MSc. Physics

| Quantum Physics

2022 – 2025 KIT
Directed by Prof. Pascal Friederich

Bachelor of Science BSc. in Physics

| Physics

2018 – 2022 KIT
Directed by Prof. Kirill Melnikov

Selected publications

From Physics to Machine Learning and Back: Part I-Learning with Inductive Biases in Prognostics and Health Management

Olga Fink et al.
Published in Reliability Engineering & System Safety in 2026

Generative models for crystalline materials

Houssam Metni et al.
Published in Advanced Materials in 2026

Teaching & PhD

Machine learning for predictive maintenance applications

CIVIL-426 
Course Book

The course aims to develop machine learning algorithms capable of efficiently detecting faults in complex industrial and infrastructure assets, isolating their root causes, and ultimately predicting their remaining useful lifetime.

Introduction to machine learning for engineers

CIVIL-226
Course Book

Machine learning is a sub-field of Artificial Intelligence that allows computers to learn from data, identify patterns and make predictions. As a fundamental building block of the Computational Thinking education at EPFL, Civil students will learn ML with civil case studies (summary generated by ML)